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Purpose:Articulatory behaviors during moments of stuttering have been understudied, largely due to the technical difficulty of collecting such data. Tracking moving articulators during stuttering requires advanced instrumentation, and eliciting stuttering in a lab setting poses challenges for experimental design. To address these difficulties, we present a novel methodology that combines real-time vocal tract magnetic resonance imaging (MRI) with a suite of connected speech tasks to elicit stuttering. Method:A high-performance 0.55 T MRI system, with a custom eight-channel upper airway coil and a spiral balanced steady-state free precession pulse sequence, was used to acquire real-time MRI speech production data from seven adults who stutter. During scans, participants performed three connected speech tasks that incorporate stuttering-inducing factors: (a) passage reading, (b) short interviews with the experimenter, and (c) picture description within a time limit. Speech tasks were interleaved with one another. Results:Each participant produced over 100 stuttered words, covering various disfluency types and linguistic features. Fluent and disfluent productions of the same words were elicited, enabling direct articulatory comparisons. Participants did not show a significant decrease in the percentage of syllables stuttered (%SS) inside the scanner compared to outside, suggesting that our protocol effectively mitigated fluency-enhancing factors during scanning. %SS in each speech task varied substantially across participants, justifying the inclusion of multiple task types. Interleaving different tasks helped maintain a stable %SS throughout. The collected real-time MRI vocal tract videos reveal meaningful articulatory behaviors during stuttering that are not detectable via acoustics alone. Conclusions:The suite of specially designed speech tasks was effective in eliciting stuttering during real-time MRI data collection. Combining these speech tasks with dynamic MRI technology offers a powerful approach to studying the articulatory mechanisms of stuttering. In addition to real-time MRI, these speech tasks have the potential to be combined with other experimental instrumentation to facilitate collecting data specifically during stuttered speech. 

Variability in speech pronunciation is widely observed across different linguistic backgrounds, which impacts modern automatic speech recognition performance. Here, we evaluate the performance of a self-supervised speech model in phoneme recognition using direct articulatory evidence. Findings indicate significant differences in phoneme recognition, especially in front vowels, between American English and Indian English speakers. To gain a deeper understanding of these differences, we conduct real-time MRI-based articulatory analysis, revealing distinct velar region patterns during the production of specific front vowels. This underscores the need to deepen the scientific understanding of self-supervised speech model variances to advance robust and inclusive speech technology.